ICHealth: large-scale digital health data on evidence-based cardiovascular medications, hospitalizations and epidemiological characteristics of heart failure patients in Brazil
Bibliographic record
Abstract
Abstract Introduction Heart failure (HF) is one of the main causes of morbimortality in Brazil. However, little is known about the characteristics of the Brazilian HF population assisted at the community level, including the use of HF medications and frequency of hospitalizations. ePHealth is an app-based large-scale digital data collection platform that currently has health data on more than 1.5 million people. Purpose To assess the use of evidence-based HF medications, hospitalizations and epidemiological characteristics of HF patients in Brazil. Methods This is an observational retrospective study of the digital health data collected using the ePHealth platform on individuals, who self-reported a diagnosis of HF. Data collected included sociodemographics, clinical data, risk factors, comorbidities, medications and hospitalizations. Results Data collected at more than 40,000 home visits on 5907 individuals with HF were analysed. Majority of them were female, aged 55 to 75 years and brown (Figure 1). About 36% were married, 20% were illiterate, 65% were retired and 66% earned ≤2 minimum wages. Mean BMI was 26.7 kg/m2 (SD 5.9), risk factors and comorbidities were frequent (Figure 1). The use of HF medications was very low (Table 1). There were 575 hospitalizations (9.7%), due to the main following reasons: probable or definite heart failure decompensation (89, 15.5%), heart attack (60, 10.4%), cardiovascular procedures (54, 9.4%) and stroke (42, 7.3%). Conclusion This data suggests that community-level use of evidence-based cardiovascular medications in a population of individuals with HF in Brazil is very low and that hospitalizations are frequent. This study also provides a better understanding of the characteristics of a population of HF individuals, using large-scale real-world data collected on a community-level via an entirely digital platform. ePHealth is a disruptive platform able to provide data on the burden of HF and other cardiovascular diseases, informing decisions on implementation of prevention and management programmes. Figure 1. HF population characteristics Funding Acknowledgement Type of funding source: Private company. Main funding source(s): ePHealth
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".